1. Field
The present invention is related to search engine technologies including human-assisted search engines and, more particularly, to a method and system for improving utilization of human searchers.
2. Description of the Related Art
In a typical human assisted search system, it is common practice to direct a query to a first available responder in order to provide a rapid response to a user submitting the query. However, such a methodology generally does not produce a high quality search result. A first available responder may not have expertise in the area requested. In addition, a search request may not include sufficient information for a search to be performed. In such a case, a first responder may be required to interact with a user to clarify a search request and obtain more information. However, such interaction is generally unstructured and does not produce a search request which can be processed rapidly. As a result, the best available human responder and/or search result may not be provided to a user.
Such an approach requires a single individual to not only be an expert at search, but also an expert at determining the intent of the customer. For example, the system of U.S. Pat. No. 7,287,021 by deSmet uses an internet search expert and several adjunct experts. Such a system may be economically unproductive as several expert searchers may be occupied with any single request. This problem becomes acute when a user is utilizing a voice, SMS or other communication with limited bandwidth.
A voice or SMS based answer system such a AQA as described in US Published Application 20070219978 takes a different approach wherein an initial query is compared to a database of queries. If the query matches, an automated answer may be provided. If no answer is found, the query is routed to a searcher who may be provided with suggested responses, and may spend up to ten minutes researching the query. This has limited viability, as evidenced by the high cost per answer charged.
An alternate approach is to analyze a search request to obtain a classification or categorization of a query. For example, categorization may be done using an automated analysis of content of a query, and/or information associated with a query. After applying such an automated analysis, a human responder may be selected to respond to a query or search request. A selected responder may be an expert in the area identified, but the query itself may be incorrectly categorized or more information may be required determine the intent of the search request due to the intrinsic ambiguity of natural language, or use of abbreviations, slang, etc. Additionally, a specialist in a given area may not be able to identify the correct subject matter of a query in order to route the query to an appropriate specialist. Valuable time of the searcher may be wasted by the need to simply return a query to a user, or to respond to a request for humor or a note of gratitude.
It may further be necessary that a search result is selected and/or modified in order to be presented to a user. For example, a user may be utilizing a device with limited capabilities such as a mobile phone which may have the capability for a short message service (SMS) or voice but no broadband connectivity. A user may desire to receive only one result at a device, but may desire to receive an additional result(s) at some future time. Such tasks are easily and rapidly done by a generalist, but may frustrate a topic expert. Expert searchers may produce an excellent response to a query in their area of knowledge, but it is difficult to reuse an expert answer as evidenced by AQA. The ‘target’ is to reuse answers, or to answer by automation at least 80% of queries. However, there is no explanation of how such a result may be achieved, which implies such a result may be difficult.
In light of the above and other problems existing in typical search systems, there is a need for a system and method for optimizing user of searchers in a human assisted search system.